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Evaluating Pricing Strategy Using e-Commerce Data: Evidence and Estimation Challenges

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  • Anindya Ghose
  • Arun Sundararajan
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    Abstract

    As Internet-based commerce becomes increasingly widespread, large data sets about the demand for and pricing of a wide variety of products become available. These present exciting new opportunities for empirical economic and business research, but also raise new statistical issues and challenges. In this article, we summarize research that aims to assess the optimality of price discrimination in the software industry using a large e-commerce panel data set gathered from Amazon.com. We describe the key parameters that relate to demand and cost that must be reliably estimated to accomplish this research successfully, and we outline our approach to estimating these parameters. This includes a method for ``reverse engineering'' actual demand levels from the sales ranks reported by Amazon, and approaches to estimating demand elasticity, variable costs and the optimality of pricing choices directly from publicly available e-commerce data. Our analysis raises many new challenges to the reliable statistical analysis of e-commerce data and we conclude with a brief summary of some salient ones.

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    File URL: http://arxiv.org/pdf/math/0609170
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    Bibliographic Info

    Paper provided by arXiv.org in its series Papers with number math/0609170.

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    Date of creation: Sep 2006
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    Publication status: Published in Statistical Science 2006, Vol. 21, No. 2, 131-142
    Handle: RePEc:arx:papers:math/0609170

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    Web page: http://arxiv.org/

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    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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    1. Brynjolfsson, Erik & Smith, Michael D. & Yu, (Jeffrey) Hu, 2003. "Consumer Surplus in the Digital Economy: Estimating the Value of Increased Product Variety at Online Booksellers," Working papers 4305-03, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    2. Yannis Bakos & Erik Brynjolfsson, 1997. "Bundling Information Goods: Pricing, Profits and Efficiency," Working Paper Series 199, MIT Center for Coordination Science.
    3. Arun Sundararajan, 2003. "Nonlinear pricing of information goods," Industrial Organization 0307003, EconWPA.
    4. Arun Sundararajan, 2003. "Managing Digital Piracy: Pricing, Protection and Welfare," Law and Economics 0307001, EconWPA.
    5. Hausman, J.A., 1994. "Valuation of New Goods Under Perfect and Imperfect Competition," Working papers 94-21, Massachusetts Institute of Technology (MIT), Department of Economics.
    6. Anindya Ghose & Vidyanand Choudhary & Tridas Mukhopadhyay & Uday Rajan, 2002. "Personalized Pricing and Quality Differentiation on the Internet," Review of Marketing Science Working Papers 2-1-1005, Berkeley Electronic Press.
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    Cited by:
    1. Chris Forman & Anindya Ghose & Avi Goldfarb, 2006. "Geography and Electronic Commerce: Measuring Convenience, Selection, and Price," Working Papers 06-15, NET Institute, revised Sep 2006.
    2. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2007. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Working Papers 07-36, NET Institute.

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